Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations7043
Missing cells30849
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory304.0 B

Variable types

Text2
Categorical8
Numeric15
Boolean13

Alerts

avg_monthly_gb_download is highly overall correlated with internet_serviceHigh correlation
avg_monthly_long_distance_charges is highly overall correlated with phone_service and 1 other fieldsHigh correlation
churn_category is highly overall correlated with churn_reason and 1 other fieldsHigh correlation
churn_reason is highly overall correlated with churn_category and 1 other fieldsHigh correlation
contract is highly overall correlated with offerHigh correlation
customer_status is highly overall correlated with churn_category and 1 other fieldsHigh correlation
device_protection_plan is highly overall correlated with internet_serviceHigh correlation
internet_service is highly overall correlated with avg_monthly_gb_download and 10 other fieldsHigh correlation
internet_type is highly overall correlated with internet_service and 1 other fieldsHigh correlation
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 1 other fieldsHigh correlation
married is highly overall correlated with number_of_referralsHigh correlation
monthly_charge is highly overall correlated with internet_service and 6 other fieldsHigh correlation
multiple_lines is highly overall correlated with phone_serviceHigh correlation
number_of_referrals is highly overall correlated with marriedHigh correlation
offer is highly overall correlated with contract and 3 other fieldsHigh correlation
online_backup is highly overall correlated with internet_serviceHigh correlation
online_security is highly overall correlated with internet_serviceHigh correlation
phone_service is highly overall correlated with avg_monthly_long_distance_charges and 2 other fieldsHigh correlation
premium_tech_support is highly overall correlated with internet_serviceHigh correlation
streaming_movies is highly overall correlated with internet_service and 2 other fieldsHigh correlation
streaming_music is highly overall correlated with internet_service and 1 other fieldsHigh correlation
streaming_tv is highly overall correlated with internet_service and 1 other fieldsHigh correlation
tenure_in_months is highly overall correlated with offer and 3 other fieldsHigh correlation
total_charges is highly overall correlated with monthly_charge and 4 other fieldsHigh correlation
total_extra_data_charges is highly overall correlated with unlimited_dataHigh correlation
total_long_distance_charges is highly overall correlated with avg_monthly_long_distance_charges and 3 other fieldsHigh correlation
total_revenue is highly overall correlated with monthly_charge and 4 other fieldsHigh correlation
unlimited_data is highly overall correlated with internet_service and 1 other fieldsHigh correlation
zip_code is highly overall correlated with latitude and 1 other fieldsHigh correlation
phone_service is highly imbalanced (54.1%) Imbalance
offer has 3877 (55.0%) missing values Missing
avg_monthly_long_distance_charges has 682 (9.7%) missing values Missing
multiple_lines has 682 (9.7%) missing values Missing
internet_type has 1526 (21.7%) missing values Missing
avg_monthly_gb_download has 1526 (21.7%) missing values Missing
online_security has 1526 (21.7%) missing values Missing
online_backup has 1526 (21.7%) missing values Missing
device_protection_plan has 1526 (21.7%) missing values Missing
premium_tech_support has 1526 (21.7%) missing values Missing
streaming_tv has 1526 (21.7%) missing values Missing
streaming_movies has 1526 (21.7%) missing values Missing
streaming_music has 1526 (21.7%) missing values Missing
unlimited_data has 1526 (21.7%) missing values Missing
churn_category has 5174 (73.5%) missing values Missing
churn_reason has 5174 (73.5%) missing values Missing
customer_id has unique values Unique
number_of_dependents has 5416 (76.9%) zeros Zeros
number_of_referrals has 3821 (54.3%) zeros Zeros
total_refunds has 6518 (92.5%) zeros Zeros
total_extra_data_charges has 6315 (89.7%) zeros Zeros
total_long_distance_charges has 682 (9.7%) zeros Zeros

Reproduction

Analysis started2024-11-21 15:10:10.794741
Analysis finished2024-11-21 15:16:45.860129
Duration6 minutes and 35.07 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
2024-11-21T15:16:48.755991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7043 ?
Unique (%)100.0%

Sample

1st row0002-ORFBO
2nd row0003-MKNFE
3rd row0004-TLHLJ
4th row0011-IGKFF
5th row0013-EXCHZ
ValueCountFrequency (%)
0002-orfbo 1
 
< 0.1%
0019-efaep 1
 
< 0.1%
0011-igkff 1
 
< 0.1%
0013-exchz 1
 
< 0.1%
0013-mhzwf 1
 
< 0.1%
0013-smeoe 1
 
< 0.1%
0014-bmaqu 1
 
< 0.1%
0015-uocoj 1
 
< 0.1%
0016-qljis 1
 
< 0.1%
0017-dinoc 1
 
< 0.1%
Other values (7033) 7033
99.9%
2024-11-21T15:16:52.532353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Male
3555 
Female
3488 

Length

Max length6
Median length4
Mean length4.990487
Min length4

Characters and Unicode

Total characters35148
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 3555
50.5%
Female 3488
49.5%

Length

2024-11-21T15:16:54.524809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T15:16:55.753209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 3555
50.5%
female 3488
49.5%

Most occurring characters

ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

age
Real number (ℝ)

Distinct62
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.509726
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:16:57.023222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q132
median46
Q360
95-th percentile75
Maximum80
Range61
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.750352
Coefficient of variation (CV)0.36014729
Kurtosis-1.0028495
Mean46.509726
Median Absolute Deviation (MAD)14
Skewness0.16218645
Sum327568
Variance280.57428
MonotonicityNot monotonic
2024-11-21T15:16:58.697614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 156
 
2.2%
47 153
 
2.2%
40 150
 
2.1%
44 148
 
2.1%
23 146
 
2.1%
56 144
 
2.0%
62 143
 
2.0%
35 142
 
2.0%
21 140
 
2.0%
33 139
 
2.0%
Other values (52) 5582
79.3%
ValueCountFrequency (%)
19 127
1.8%
20 127
1.8%
21 140
2.0%
22 130
1.8%
23 146
2.1%
24 109
1.5%
25 138
2.0%
26 115
1.6%
27 132
1.9%
28 119
1.7%
ValueCountFrequency (%)
80 66
0.9%
79 76
1.1%
78 63
0.9%
77 72
1.0%
76 69
1.0%
75 74
1.1%
74 76
1.1%
73 85
1.2%
72 58
0.8%
71 68
1.0%

married
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3641 
True
3402 
ValueCountFrequency (%)
False 3641
51.7%
True 3402
48.3%
2024-11-21T15:17:00.135838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

number_of_dependents
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46869232
Minimum0
Maximum9
Zeros5416
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:01.036744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96280195
Coefficient of variation (CV)2.0542303
Kurtosis4.4463579
Mean0.46869232
Median Absolute Deviation (MAD)0
Skewness2.109932
Sum3301
Variance0.9269876
MonotonicityNot monotonic
2024-11-21T15:17:02.809979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
5 10
 
0.1%
4 9
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
4 9
 
0.1%
5 10
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 3
 
< 0.1%
5 10
 
0.1%
4 9
 
0.1%
3 517
 
7.3%
2 531
 
7.5%
1 553
 
7.9%
0 5416
76.9%

city
Text

Distinct1106
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:05.293313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length9.2034644
Min length3

Characters and Unicode

Total characters64820
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrazier Park
2nd rowGlendale
3rd rowCosta Mesa
4th rowMartinez
5th rowCamarillo
ValueCountFrequency (%)
san 718
 
6.9%
los 337
 
3.3%
angeles 293
 
2.8%
diego 285
 
2.8%
santa 181
 
1.8%
valley 171
 
1.7%
beach 169
 
1.6%
city 150
 
1.5%
sacramento 116
 
1.1%
jose 112
 
1.1%
Other values (1110) 7807
75.5%
2024-11-21T15:17:10.180380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

zip_code
Real number (ℝ)

High correlation 

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93486.071
Minimum90001
Maximum96150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:11.291592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90241.1
Q192101
median93518
Q395329
95-th percentile96020.9
Maximum96150
Range6149
Interquartile range (IQR)3228

Descriptive statistics

Standard deviation1856.7675
Coefficient of variation (CV)0.019861435
Kurtosis-1.1739154
Mean93486.071
Median Absolute Deviation (MAD)1605
Skewness-0.20961512
Sum6.584224 × 108
Variance3447585.6
MonotonicityNot monotonic
2024-11-21T15:17:13.177286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92028 43
 
0.6%
92027 38
 
0.5%
92122 36
 
0.5%
92117 34
 
0.5%
92126 32
 
0.5%
92592 30
 
0.4%
92109 27
 
0.4%
92130 22
 
0.3%
92121 20
 
0.3%
92129 16
 
0.2%
Other values (1616) 6745
95.8%
ValueCountFrequency (%)
90001 4
0.1%
90002 4
0.1%
90003 5
0.1%
90004 5
0.1%
90005 4
0.1%
90006 5
0.1%
90007 5
0.1%
90008 5
0.1%
90010 4
0.1%
90011 5
0.1%
ValueCountFrequency (%)
96150 2
< 0.1%
96148 4
0.1%
96146 4
0.1%
96145 3
< 0.1%
96143 4
0.1%
96142 3
< 0.1%
96141 3
< 0.1%
96140 4
0.1%
96137 4
0.1%
96136 4
0.1%

latitude
Real number (ℝ)

High correlation 

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.197455
Minimum32.555828
Maximum41.962127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:15.899125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32.555828
5-th percentile32.886925
Q133.990646
median36.205465
Q338.161321
95-th percentile40.497425
Maximum41.962127
Range9.406299
Interquartile range (IQR)4.170675

Descriptive statistics

Standard deviation2.4689287
Coefficient of variation (CV)0.068207245
Kurtosis-1.1605061
Mean36.197455
Median Absolute Deviation (MAD)2.169863
Skewness0.31480427
Sum254938.67
Variance6.0956088
MonotonicityNot monotonic
2024-11-21T15:17:17.552977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.362575 43
 
0.6%
33.141265 38
 
0.5%
32.85723 36
 
0.5%
32.825086 34
 
0.5%
32.886925 32
 
0.5%
33.507255 30
 
0.4%
32.787836 27
 
0.4%
32.957195 22
 
0.3%
32.898613 20
 
0.3%
32.961064 16
 
0.2%
Other values (1616) 6745
95.8%
ValueCountFrequency (%)
32.555828 5
0.1%
32.578103 4
0.1%
32.579134 4
0.1%
32.587557 5
0.1%
32.605012 4
0.1%
32.607964 5
0.1%
32.619465 5
0.1%
32.622999 4
0.1%
32.636792 4
0.1%
32.64164 5
0.1%
ValueCountFrequency (%)
41.962127 4
0.1%
41.950683 4
0.1%
41.949216 4
0.1%
41.932207 3
< 0.1%
41.924174 3
< 0.1%
41.867908 4
0.1%
41.831901 4
0.1%
41.816595 4
0.1%
41.813521 4
0.1%
41.769709 4
0.1%

longitude
Real number (ℝ)

High correlation 

Distinct1625
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.75668
Minimum-124.30137
Maximum-114.1929
Zeros0
Zeros (%)0.0%
Negative7043
Negative (%)100.0%
Memory size55.1 KiB
2024-11-21T15:17:19.430089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-124.30137
5-th percentile-122.9755
Q1-121.78809
median-119.59529
Q3-117.9698
95-th percentile-116.87326
Maximum-114.1929
Range10.108471
Interquartile range (IQR)3.818295

Descriptive statistics

Standard deviation2.1544251
Coefficient of variation (CV)-0.01799002
Kurtosis-1.1912906
Mean-119.75668
Median Absolute Deviation (MAD)1.848851
Skewness-0.091931635
Sum-843446.32
Variance4.6415475
MonotonicityNot monotonic
2024-11-21T15:17:21.895958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-117.299644 43
 
0.6%
-116.967221 38
 
0.5%
-117.209774 36
 
0.5%
-117.199424 34
 
0.5%
-117.152162 32
 
0.5%
-117.029473 30
 
0.4%
-117.232376 27
 
0.4%
-117.202542 22
 
0.3%
-117.202937 20
 
0.3%
-117.134917 16
 
0.2%
Other values (1615) 6745
95.8%
ValueCountFrequency (%)
-124.301372 4
0.1%
-124.240051 4
0.1%
-124.217378 4
0.1%
-124.210902 4
0.1%
-124.189977 4
0.1%
-124.163234 4
0.1%
-124.15428 4
0.1%
-124.121504 4
0.1%
-124.108897 4
0.1%
-124.098739 4
0.1%
ValueCountFrequency (%)
-114.192901 4
0.1%
-114.36514 5
0.1%
-114.702256 4
0.1%
-114.71612 4
0.1%
-114.758334 5
0.1%
-114.850784 4
0.1%
-115.152865 2
 
< 0.1%
-115.191857 5
0.1%
-115.257009 5
0.1%
-115.287901 4
0.1%

number_of_referrals
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9518671
Minimum0
Maximum11
Zeros3821
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:23.589299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0011993
Coefficient of variation (CV)1.5376043
Kurtosis0.72196393
Mean1.9518671
Median Absolute Deviation (MAD)0
Skewness1.4460596
Sum13747
Variance9.0071972
MonotonicityNot monotonic
2024-11-21T15:17:25.119469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
5 264
 
3.7%
3 255
 
3.6%
7 248
 
3.5%
9 238
 
3.4%
2 236
 
3.4%
4 236
 
3.4%
10 223
 
3.2%
6 221
 
3.1%
Other values (2) 215
 
3.1%
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
2 236
 
3.4%
3 255
 
3.6%
4 236
 
3.4%
5 264
 
3.7%
6 221
 
3.1%
7 248
 
3.5%
8 213
 
3.0%
9 238
 
3.4%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 223
3.2%
9 238
3.4%
8 213
3.0%
7 248
3.5%
6 221
3.1%
5 264
3.7%
4 236
3.4%
3 255
3.6%
2 236
3.4%

tenure_in_months
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.386767
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:26.242398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.542061
Coefficient of variation (CV)0.75778052
Kurtosis-1.3870524
Mean32.386767
Median Absolute Deviation (MAD)22
Skewness0.24054261
Sum228100
Variance602.31276
MonotonicityNot monotonic
2024-11-21T15:17:27.343634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 613
 
8.7%
72 362
 
5.1%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
71 170
 
2.4%
5 133
 
1.9%
7 131
 
1.9%
10 127
 
1.8%
8 123
 
1.7%
Other values (62) 4770
67.7%
ValueCountFrequency (%)
1 613
8.7%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
5 133
 
1.9%
6 110
 
1.6%
7 131
 
1.9%
8 123
 
1.7%
9 119
 
1.7%
10 127
 
1.8%
ValueCountFrequency (%)
72 362
5.1%
71 170
2.4%
70 119
 
1.7%
69 95
 
1.3%
68 100
 
1.4%
67 98
 
1.4%
66 89
 
1.3%
65 76
 
1.1%
64 80
 
1.1%
63 72
 
1.0%

offer
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.2%
Missing3877
Missing (%)55.0%
Memory size55.1 KiB
Offer B
824 
Offer E
805 
Offer D
602 
Offer A
520 
Offer C
415 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters22162
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffer E
2nd rowOffer D
3rd rowOffer E
4th rowOffer A
5th rowOffer B

Common Values

ValueCountFrequency (%)
Offer B 824
 
11.7%
Offer E 805
 
11.4%
Offer D 602
 
8.5%
Offer A 520
 
7.4%
Offer C 415
 
5.9%
(Missing) 3877
55.0%

Length

2024-11-21T15:17:28.507615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T15:17:29.520397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
offer 3166
50.0%
b 824
 
13.0%
e 805
 
12.7%
d 602
 
9.5%
a 520
 
8.2%
c 415
 
6.6%

Most occurring characters

ValueCountFrequency (%)
f 6332
28.6%
O 3166
14.3%
e 3166
14.3%
r 3166
14.3%
3166
14.3%
B 824
 
3.7%
E 805
 
3.6%
D 602
 
2.7%
A 520
 
2.3%
C 415
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 6332
28.6%
O 3166
14.3%
e 3166
14.3%
r 3166
14.3%
3166
14.3%
B 824
 
3.7%
E 805
 
3.6%
D 602
 
2.7%
A 520
 
2.3%
C 415
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 6332
28.6%
O 3166
14.3%
e 3166
14.3%
r 3166
14.3%
3166
14.3%
B 824
 
3.7%
E 805
 
3.6%
D 602
 
2.7%
A 520
 
2.3%
C 415
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 6332
28.6%
O 3166
14.3%
e 3166
14.3%
r 3166
14.3%
3166
14.3%
B 824
 
3.7%
E 805
 
3.6%
D 602
 
2.7%
A 520
 
2.3%
C 415
 
1.9%

phone_service
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True 6361
90.3%
False 682
 
9.7%
2024-11-21T15:17:30.654027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

avg_monthly_long_distance_charges
Real number (ℝ)

High correlation  Missing 

Distinct3583
Distinct (%)56.3%
Missing682
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean25.420517
Minimum1.01
Maximum49.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:32.064219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile3.27
Q113.05
median25.69
Q337.68
95-th percentile47.64
Maximum49.99
Range48.98
Interquartile range (IQR)24.63

Descriptive statistics

Standard deviation14.200374
Coefficient of variation (CV)0.55861859
Kurtosis-1.2059985
Mean25.420517
Median Absolute Deviation (MAD)12.29
Skewness-0.0019709679
Sum161699.91
Variance201.65061
MonotonicityNot monotonic
2024-11-21T15:17:33.452154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.26 7
 
0.1%
30.07 6
 
0.1%
45.92 6
 
0.1%
30.09 6
 
0.1%
42.55 6
 
0.1%
49.51 6
 
0.1%
22.56 6
 
0.1%
22.83 6
 
0.1%
41.93 6
 
0.1%
18.42 6
 
0.1%
Other values (3573) 6300
89.5%
(Missing) 682
 
9.7%
ValueCountFrequency (%)
1.01 1
 
< 0.1%
1.02 3
< 0.1%
1.03 1
 
< 0.1%
1.05 1
 
< 0.1%
1.06 1
 
< 0.1%
1.07 1
 
< 0.1%
1.08 2
< 0.1%
1.09 2
< 0.1%
1.1 1
 
< 0.1%
1.12 3
< 0.1%
ValueCountFrequency (%)
49.99 1
 
< 0.1%
49.98 3
< 0.1%
49.96 2
< 0.1%
49.95 2
< 0.1%
49.94 1
 
< 0.1%
49.92 1
 
< 0.1%
49.91 3
< 0.1%
49.9 3
< 0.1%
49.88 1
 
< 0.1%
49.87 1
 
< 0.1%

multiple_lines
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing682
Missing (%)9.7%
Memory size13.9 KiB
False
3390 
True
2971 
(Missing)
682 
ValueCountFrequency (%)
False 3390
48.1%
True 2971
42.2%
(Missing) 682
 
9.7%
2024-11-21T15:17:34.713894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

internet_service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
5517 
False
1526 
ValueCountFrequency (%)
True 5517
78.3%
False 1526
 
21.7%
2024-11-21T15:17:35.638295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

internet_type
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing1526
Missing (%)21.7%
Memory size55.1 KiB
Fiber Optic
3035 
DSL
1652 
Cable
830 

Length

Max length11
Median length11
Mean length7.7018307
Min length3

Characters and Unicode

Total characters42491
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCable
2nd rowCable
3rd rowFiber Optic
4th rowFiber Optic
5th rowFiber Optic

Common Values

ValueCountFrequency (%)
Fiber Optic 3035
43.1%
DSL 1652
23.5%
Cable 830
 
11.8%
(Missing) 1526
21.7%

Length

2024-11-21T15:17:36.584843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T15:17:38.332059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
fiber 3035
35.5%
optic 3035
35.5%
dsl 1652
19.3%
cable 830
 
9.7%

Most occurring characters

ValueCountFrequency (%)
i 6070
14.3%
b 3865
9.1%
e 3865
9.1%
F 3035
7.1%
r 3035
7.1%
3035
7.1%
O 3035
7.1%
p 3035
7.1%
t 3035
7.1%
c 3035
7.1%
Other values (6) 7446
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6070
14.3%
b 3865
9.1%
e 3865
9.1%
F 3035
7.1%
r 3035
7.1%
3035
7.1%
O 3035
7.1%
p 3035
7.1%
t 3035
7.1%
c 3035
7.1%
Other values (6) 7446
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6070
14.3%
b 3865
9.1%
e 3865
9.1%
F 3035
7.1%
r 3035
7.1%
3035
7.1%
O 3035
7.1%
p 3035
7.1%
t 3035
7.1%
c 3035
7.1%
Other values (6) 7446
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6070
14.3%
b 3865
9.1%
e 3865
9.1%
F 3035
7.1%
r 3035
7.1%
3035
7.1%
O 3035
7.1%
p 3035
7.1%
t 3035
7.1%
c 3035
7.1%
Other values (6) 7446
17.5%

avg_monthly_gb_download
Real number (ℝ)

High correlation  Missing 

Distinct49
Distinct (%)0.9%
Missing1526
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean26.189958
Minimum2
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:17:40.145159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q113
median21
Q330
95-th percentile71
Maximum85
Range83
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.586585
Coefficient of variation (CV)0.74786623
Kurtosis0.63684154
Mean26.189958
Median Absolute Deviation (MAD)9
Skewness1.184056
Sum144490
Variance383.63432
MonotonicityNot monotonic
2024-11-21T15:17:41.509570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
19 220
 
3.1%
27 199
 
2.8%
30 193
 
2.7%
59 192
 
2.7%
26 191
 
2.7%
23 179
 
2.5%
22 172
 
2.4%
21 171
 
2.4%
18 164
 
2.3%
13 164
 
2.3%
Other values (39) 3672
52.1%
(Missing) 1526
21.7%
ValueCountFrequency (%)
2 116
1.6%
3 130
1.8%
4 129
1.8%
5 114
1.6%
6 114
1.6%
7 116
1.6%
8 120
1.7%
9 116
1.6%
10 132
1.9%
11 145
2.1%
ValueCountFrequency (%)
85 48
 
0.7%
82 43
 
0.6%
76 58
 
0.8%
75 15
 
0.2%
73 81
1.2%
71 42
 
0.6%
69 75
 
1.1%
59 192
2.7%
58 45
 
0.6%
57 34
 
0.5%

online_security
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3498 
True
2019 
(Missing)
1526 
ValueCountFrequency (%)
False 3498
49.7%
True 2019
28.7%
(Missing) 1526
21.7%
2024-11-21T15:17:42.482424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

online_backup
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3088 
True
2429 
(Missing)
1526 
ValueCountFrequency (%)
False 3088
43.8%
True 2429
34.5%
(Missing) 1526
21.7%
2024-11-21T15:17:43.279995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

device_protection_plan
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3095 
True
2422 
(Missing)
1526 
ValueCountFrequency (%)
False 3095
43.9%
True 2422
34.4%
(Missing) 1526
21.7%
2024-11-21T15:17:44.107740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

premium_tech_support
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3473 
True
2044 
(Missing)
1526 
ValueCountFrequency (%)
False 3473
49.3%
True 2044
29.0%
(Missing) 1526
21.7%
2024-11-21T15:17:44.866862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

streaming_tv
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
2810 
True
2707 
(Missing)
1526 
ValueCountFrequency (%)
False 2810
39.9%
True 2707
38.4%
(Missing) 1526
21.7%
2024-11-21T15:17:45.552603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

streaming_movies
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
2785 
True
2732 
(Missing)
1526 
ValueCountFrequency (%)
False 2785
39.5%
True 2732
38.8%
(Missing) 1526
21.7%
2024-11-21T15:17:46.382589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

streaming_music
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3029 
True
2488 
(Missing)
1526 
ValueCountFrequency (%)
False 3029
43.0%
True 2488
35.3%
(Missing) 1526
21.7%
2024-11-21T15:17:47.251930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

unlimited_data
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
True
4745 
False
772 
(Missing)
1526 
ValueCountFrequency (%)
True 4745
67.4%
False 772
 
11.0%
(Missing) 1526
 
21.7%
2024-11-21T15:17:47.954300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

contract
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Month-to-Month
3610 
Two Year
1883 
One Year
1550 

Length

Max length14
Median length14
Mean length11.075394
Min length8

Characters and Unicode

Total characters78004
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOne Year
2nd rowMonth-to-Month
3rd rowMonth-to-Month
4th rowMonth-to-Month
5th rowMonth-to-Month

Common Values

ValueCountFrequency (%)
Month-to-Month 3610
51.3%
Two Year 1883
26.7%
One Year 1550
22.0%

Length

2024-11-21T15:17:49.013171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T15:17:49.967381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month 3610
34.5%
year 3433
32.8%
two 1883
18.0%
one 1550
14.8%

Most occurring characters

ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True 4171
59.2%
False 2872
40.8%
2024-11-21T15:17:51.348925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

payment_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Bank Withdrawal
3909 
Credit Card
2749 
Mailed Check
 
385

Length

Max length15
Median length15
Mean length13.274741
Min length11

Characters and Unicode

Total characters93494
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCredit Card
3rd rowBank Withdrawal
4th rowBank Withdrawal
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Bank Withdrawal 3909
55.5%
Credit Card 2749
39.0%
Mailed Check 385
 
5.5%

Length

2024-11-21T15:17:53.351819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T15:17:55.794184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
bank 3909
27.8%
withdrawal 3909
27.8%
credit 2749
19.5%
card 2749
19.5%
mailed 385
 
2.7%
check 385
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

monthly_charge
Real number (ℝ)

High correlation 

Distinct1591
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.596131
Minimum-10
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative120
Negative (%)1.7%
Memory size55.1 KiB
2024-11-21T15:18:00.480050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile19.5
Q130.4
median70.05
Q389.75
95-th percentile107.195
Maximum118.75
Range128.75
Interquartile range (IQR)59.35

Descriptive statistics

Standard deviation31.204743
Coefficient of variation (CV)0.49067046
Kurtosis-1.1257896
Mean63.596131
Median Absolute Deviation (MAD)24.5
Skewness-0.27539383
Sum447907.55
Variance973.73599
MonotonicityNot monotonic
2024-11-21T15:18:05.492813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.05 60
 
0.9%
19.85 45
 
0.6%
19.95 44
 
0.6%
19.9 44
 
0.6%
20 43
 
0.6%
19.65 42
 
0.6%
19.7 40
 
0.6%
20.25 39
 
0.6%
20.15 39
 
0.6%
19.55 39
 
0.6%
Other values (1581) 6608
93.8%
ValueCountFrequency (%)
-10 13
0.2%
-9 9
0.1%
-8 12
0.2%
-7 16
0.2%
-6 6
 
0.1%
-5 11
0.2%
-4 17
0.2%
-3 13
0.2%
-2 10
0.1%
-1 13
0.2%
ValueCountFrequency (%)
118.75 1
< 0.1%
118.65 1
< 0.1%
118.6 2
< 0.1%
118.35 1
< 0.1%
118.2 1
< 0.1%
117.8 1
< 0.1%
117.6 1
< 0.1%
117.5 1
< 0.1%
117.45 1
< 0.1%
117.35 1
< 0.1%

total_charges
Real number (ℝ)

High correlation 

Distinct6540
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2280.3813
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:18:07.945595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.65
Q1400.15
median1394.55
Q33786.6
95-th percentile6921.025
Maximum8684.8
Range8666
Interquartile range (IQR)3386.45

Descriptive statistics

Standard deviation2266.2205
Coefficient of variation (CV)0.99379016
Kurtosis-0.22769266
Mean2280.3813
Median Absolute Deviation (MAD)1219.75
Skewness0.96379109
Sum16060725
Variance5135755.2
MonotonicityNot monotonic
2024-11-21T15:18:10.912801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.2 11
 
0.2%
19.75 9
 
0.1%
19.9 8
 
0.1%
20.05 8
 
0.1%
19.65 8
 
0.1%
19.55 7
 
0.1%
45.3 7
 
0.1%
20.15 6
 
0.1%
19.45 6
 
0.1%
20.25 6
 
0.1%
Other values (6530) 6967
98.9%
ValueCountFrequency (%)
18.8 1
 
< 0.1%
18.85 2
< 0.1%
18.9 1
 
< 0.1%
19 1
 
< 0.1%
19.05 1
 
< 0.1%
19.1 3
< 0.1%
19.15 1
 
< 0.1%
19.2 4
0.1%
19.25 3
< 0.1%
19.3 4
0.1%
ValueCountFrequency (%)
8684.8 1
< 0.1%
8672.45 1
< 0.1%
8670.1 1
< 0.1%
8594.4 1
< 0.1%
8564.75 1
< 0.1%
8547.15 1
< 0.1%
8543.25 1
< 0.1%
8529.5 1
< 0.1%
8496.7 1
< 0.1%
8477.7 1
< 0.1%

total_refunds
Real number (ℝ)

Zeros 

Distinct500
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9621823
Minimum0
Maximum49.79
Zeros6518
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:18:14.413547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18.149
Maximum49.79
Range49.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9026144
Coefficient of variation (CV)4.0274618
Kurtosis18.350658
Mean1.9621823
Median Absolute Deviation (MAD)0
Skewness4.3285167
Sum13819.65
Variance62.451314
MonotonicityNot monotonic
2024-11-21T15:18:16.304864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6518
92.5%
16.56 2
 
< 0.1%
8.74 2
 
< 0.1%
1.31 2
 
< 0.1%
41.74 2
 
< 0.1%
25.67 2
 
< 0.1%
29.76 2
 
< 0.1%
18.55 2
 
< 0.1%
15.41 2
 
< 0.1%
27.6 2
 
< 0.1%
Other values (490) 507
 
7.2%
ValueCountFrequency (%)
0 6518
92.5%
1.01 1
 
< 0.1%
1.09 1
 
< 0.1%
1.27 1
 
< 0.1%
1.31 2
 
< 0.1%
1.48 1
 
< 0.1%
1.65 1
 
< 0.1%
1.66 1
 
< 0.1%
1.69 1
 
< 0.1%
1.83 1
 
< 0.1%
ValueCountFrequency (%)
49.79 1
< 0.1%
49.76 1
< 0.1%
49.57 2
< 0.1%
49.53 1
< 0.1%
49.51 1
< 0.1%
49.38 1
< 0.1%
49.37 1
< 0.1%
49.24 1
< 0.1%
49.23 1
< 0.1%
49.22 1
< 0.1%

total_extra_data_charges
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8607128
Minimum0
Maximum150
Zeros6315
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:18:19.012334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile60
Maximum150
Range150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.104978
Coefficient of variation (CV)3.6592376
Kurtosis16.458874
Mean6.8607128
Median Absolute Deviation (MAD)0
Skewness4.0912092
Sum48320
Variance630.25992
MonotonicityNot monotonic
2024-11-21T15:18:21.077960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6315
89.7%
10 138
 
2.0%
40 62
 
0.9%
30 58
 
0.8%
20 51
 
0.7%
80 47
 
0.7%
100 44
 
0.6%
50 43
 
0.6%
150 42
 
0.6%
130 40
 
0.6%
Other values (6) 203
 
2.9%
ValueCountFrequency (%)
0 6315
89.7%
10 138
 
2.0%
20 51
 
0.7%
30 58
 
0.8%
40 62
 
0.9%
50 43
 
0.6%
60 36
 
0.5%
70 34
 
0.5%
80 47
 
0.7%
90 35
 
0.5%
ValueCountFrequency (%)
150 42
0.6%
140 38
0.5%
130 40
0.6%
120 28
0.4%
110 32
0.5%
100 44
0.6%
90 35
0.5%
80 47
0.7%
70 34
0.5%
60 36
0.5%

total_long_distance_charges
Real number (ℝ)

High correlation  Zeros 

Distinct6068
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.09926
Minimum0
Maximum3564.72
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:18:23.011432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.545
median401.44
Q31191.1
95-th percentile2577.877
Maximum3564.72
Range3564.72
Interquartile range (IQR)1120.555

Descriptive statistics

Standard deviation846.66005
Coefficient of variation (CV)1.1302375
Kurtosis0.64409208
Mean749.09926
Median Absolute Deviation (MAD)382.12
Skewness1.238282
Sum5275906.1
Variance716833.25
MonotonicityNot monotonic
2024-11-21T15:18:24.915928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
9.7%
15.6 4
 
0.1%
48.96 4
 
0.1%
22.86 4
 
0.1%
597.6 3
 
< 0.1%
2077.92 3
 
< 0.1%
198 3
 
< 0.1%
15.28 3
 
< 0.1%
808.08 3
 
< 0.1%
41.1 3
 
< 0.1%
Other values (6058) 6331
89.9%
ValueCountFrequency (%)
0 682
9.7%
1.13 1
 
< 0.1%
1.15 1
 
< 0.1%
1.17 1
 
< 0.1%
1.23 1
 
< 0.1%
1.28 1
 
< 0.1%
1.47 1
 
< 0.1%
1.48 1
 
< 0.1%
1.5 1
 
< 0.1%
1.59 1
 
< 0.1%
ValueCountFrequency (%)
3564.72 1
< 0.1%
3564 1
< 0.1%
3536.64 1
< 0.1%
3515.92 1
< 0.1%
3508.82 1
< 0.1%
3501.72 1
< 0.1%
3493.44 1
< 0.1%
3492.72 1
< 0.1%
3487.68 1
< 0.1%
3482.64 1
< 0.1%

total_revenue
Real number (ℝ)

High correlation 

Distinct6975
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3034.3791
Minimum21.36
Maximum11979.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-11-21T15:18:27.593093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum21.36
5-th percentile78.452
Q1605.61
median2108.64
Q34801.145
95-th percentile8747.041
Maximum11979.34
Range11957.98
Interquartile range (IQR)4195.535

Descriptive statistics

Standard deviation2865.2045
Coefficient of variation (CV)0.9442474
Kurtosis-0.20345739
Mean3034.3791
Median Absolute Deviation (MAD)1767.61
Skewness0.91941027
Sum21371132
Variance8209397.1
MonotonicityNot monotonic
2024-11-21T15:18:31.726877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.8 3
 
< 0.1%
116.27 3
 
< 0.1%
68.41 3
 
< 0.1%
66.56 3
 
< 0.1%
3386.4 2
 
< 0.1%
174.93 2
 
< 0.1%
608.85 2
 
< 0.1%
712.85 2
 
< 0.1%
3423.5 2
 
< 0.1%
88.75 2
 
< 0.1%
Other values (6965) 7019
99.7%
ValueCountFrequency (%)
21.36 1
< 0.1%
21.4 1
< 0.1%
21.61 1
< 0.1%
22.08 1
< 0.1%
22.12 1
< 0.1%
22.25 1
< 0.1%
22.28 1
< 0.1%
22.54 1
< 0.1%
23.24 2
< 0.1%
23.28 1
< 0.1%
ValueCountFrequency (%)
11979.34 1
< 0.1%
11868.34 1
< 0.1%
11795.78 1
< 0.1%
11688.9 1
< 0.1%
11634.53 1
< 0.1%
11596.99 1
< 0.1%
11564.37 1
< 0.1%
11529.54 1
< 0.1%
11514.81 1
< 0.1%
11501.82 1
< 0.1%

customer_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Stayed
4720 
Churned
1869 
Joined
 
454

Length

Max length7
Median length6
Mean length6.2653699
Min length6

Characters and Unicode

Total characters44127
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStayed
2nd rowStayed
3rd rowChurned
4th rowChurned
5th rowChurned

Common Values

ValueCountFrequency (%)
Stayed 4720
67.0%
Churned 1869
 
26.5%
Joined 454
 
6.4%

Length

2024-11-21T15:18:33.335654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T15:18:34.404270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
stayed 4720
67.0%
churned 1869
 
26.5%
joined 454
 
6.4%

Most occurring characters

ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
S 4720
10.7%
t 4720
10.7%
a 4720
10.7%
y 4720
10.7%
n 2323
 
5.3%
C 1869
 
4.2%
h 1869
 
4.2%
u 1869
 
4.2%
Other values (4) 3231
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
S 4720
10.7%
t 4720
10.7%
a 4720
10.7%
y 4720
10.7%
n 2323
 
5.3%
C 1869
 
4.2%
h 1869
 
4.2%
u 1869
 
4.2%
Other values (4) 3231
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
S 4720
10.7%
t 4720
10.7%
a 4720
10.7%
y 4720
10.7%
n 2323
 
5.3%
C 1869
 
4.2%
h 1869
 
4.2%
u 1869
 
4.2%
Other values (4) 3231
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7043
16.0%
d 7043
16.0%
S 4720
10.7%
t 4720
10.7%
a 4720
10.7%
y 4720
10.7%
n 2323
 
5.3%
C 1869
 
4.2%
h 1869
 
4.2%
u 1869
 
4.2%
Other values (4) 3231
7.3%

churn_category
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.3%
Missing5174
Missing (%)73.5%
Memory size55.1 KiB
Competitor
841 
Dissatisfaction
321 
Attitude
314 
Price
211 
Other
182 

Length

Max length15
Median length10
Mean length9.4713751
Min length5

Characters and Unicode

Total characters17702
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor
2nd rowDissatisfaction
3rd rowDissatisfaction
4th rowDissatisfaction
5th rowCompetitor

Common Values

ValueCountFrequency (%)
Competitor 841
 
11.9%
Dissatisfaction 321
 
4.6%
Attitude 314
 
4.5%
Price 211
 
3.0%
Other 182
 
2.6%
(Missing) 5174
73.5%

Length

2024-11-21T15:18:35.518442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T15:18:37.406115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
competitor 841
45.0%
dissatisfaction 321
 
17.2%
attitude 314
 
16.8%
price 211
 
11.3%
other 182
 
9.7%

Most occurring characters

ValueCountFrequency (%)
t 3448
19.5%
i 2329
13.2%
o 2003
11.3%
e 1548
8.7%
r 1234
 
7.0%
s 963
 
5.4%
C 841
 
4.8%
m 841
 
4.8%
p 841
 
4.8%
a 642
 
3.6%
Other values (10) 3012
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3448
19.5%
i 2329
13.2%
o 2003
11.3%
e 1548
8.7%
r 1234
 
7.0%
s 963
 
5.4%
C 841
 
4.8%
m 841
 
4.8%
p 841
 
4.8%
a 642
 
3.6%
Other values (10) 3012
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3448
19.5%
i 2329
13.2%
o 2003
11.3%
e 1548
8.7%
r 1234
 
7.0%
s 963
 
5.4%
C 841
 
4.8%
m 841
 
4.8%
p 841
 
4.8%
a 642
 
3.6%
Other values (10) 3012
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3448
19.5%
i 2329
13.2%
o 2003
11.3%
e 1548
8.7%
r 1234
 
7.0%
s 963
 
5.4%
C 841
 
4.8%
m 841
 
4.8%
p 841
 
4.8%
a 642
 
3.6%
Other values (10) 3012
17.0%

churn_reason
Categorical

High correlation  Missing 

Distinct20
Distinct (%)1.1%
Missing5174
Missing (%)73.5%
Memory size55.1 KiB
Competitor had better devices
313 
Competitor made better offer
311 
Attitude of support person
220 
Don't know
130 
Competitor offered more data
117 
Other values (15)
778 

Length

Max length41
Median length32
Mean length25.256822
Min length5

Characters and Unicode

Total characters47205
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor had better devices
2nd rowProduct dissatisfaction
3rd rowNetwork reliability
4th rowLimited range of services
5th rowCompetitor made better offer

Common Values

ValueCountFrequency (%)
Competitor had better devices 313
 
4.4%
Competitor made better offer 311
 
4.4%
Attitude of support person 220
 
3.1%
Don't know 130
 
1.8%
Competitor offered more data 117
 
1.7%
Competitor offered higher download speeds 100
 
1.4%
Attitude of service provider 94
 
1.3%
Price too high 78
 
1.1%
Product dissatisfaction 77
 
1.1%
Network reliability 72
 
1.0%
Other values (10) 357
 
5.1%
(Missing) 5174
73.5%

Length

2024-11-21T15:18:39.207167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitor 841
 
12.6%
better 624
 
9.4%
of 453
 
6.8%
attitude 314
 
4.7%
had 313
 
4.7%
devices 313
 
4.7%
made 311
 
4.7%
offer 311
 
4.7%
support 263
 
4.0%
person 220
 
3.3%
Other values (37) 2694
40.5%

Most occurring characters

ValueCountFrequency (%)
e 6138
13.0%
t 5212
11.0%
4788
10.1%
o 4650
 
9.9%
r 3698
 
7.8%
i 2918
 
6.2%
d 2538
 
5.4%
s 1917
 
4.1%
p 1896
 
4.0%
a 1816
 
3.8%
Other values (27) 11634
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6138
13.0%
t 5212
11.0%
4788
10.1%
o 4650
 
9.9%
r 3698
 
7.8%
i 2918
 
6.2%
d 2538
 
5.4%
s 1917
 
4.1%
p 1896
 
4.0%
a 1816
 
3.8%
Other values (27) 11634
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6138
13.0%
t 5212
11.0%
4788
10.1%
o 4650
 
9.9%
r 3698
 
7.8%
i 2918
 
6.2%
d 2538
 
5.4%
s 1917
 
4.1%
p 1896
 
4.0%
a 1816
 
3.8%
Other values (27) 11634
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6138
13.0%
t 5212
11.0%
4788
10.1%
o 4650
 
9.9%
r 3698
 
7.8%
i 2918
 
6.2%
d 2538
 
5.4%
s 1917
 
4.1%
p 1896
 
4.0%
a 1816
 
3.8%
Other values (27) 11634
24.6%

Interactions

2024-11-21T15:15:55.616262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:53.444256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:03.907050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:20.371988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:28.524741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:36.324031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:44.383420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:53.846130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:11.394979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:36.579174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:00.690744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:19.326842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:38.087816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:00.598794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:51.335451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:58.353810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:54.115093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:05.658386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:20.900901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:29.193280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:36.831598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:44.930780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:54.428406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:14.206651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:38.068346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:01.618038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:20.159558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:38.701721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:06.604649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:54.372098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:00.250673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:54.723666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:06.315446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:21.464002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:29.734181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:37.454192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:45.344297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:55.016685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:16.581969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:38.873911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:02.185654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:20.688917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:39.404675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:13.143031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:58.476004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:01.829912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:55.244768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:06.856494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:21.930850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:30.212138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:38.000406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:45.884845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:55.509976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:17.276567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:40.059926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:02.668626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:22.247663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:39.996630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:17.400313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:02.591846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:03.396127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:55.711473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:07.388470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:22.436728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:30.627737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:38.472190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:46.540621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:56.061867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:17.876528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:40.985263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:03.336920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:22.737808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:40.450033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:20.826446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:06.272196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:05.741890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:56.194202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:08.032927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:22.943608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:31.180314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:38.970738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:47.215294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:56.601366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:18.978738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:41.706181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:05.133394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:23.354270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:42.813806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:22.332295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:11.160206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:07.342244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:56.686178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:09.741209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:23.442896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:31.563771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:39.382527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:47.895987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:57.174936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:20.046705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:44.220744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:07.214644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:25.113688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:44.083995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:24.936902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:19.031153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:08.744476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:57.272516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:11.451383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:24.088314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:32.082472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:39.889682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:48.423971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:57.881379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:20.678673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:47.927628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:09.374240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:26.951035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:45.094265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:26.882235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:22.859561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:10.424437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:57.770547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:12.708794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:24.599315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:32.608665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:40.365411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:49.496959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:59.750480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:21.962677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:49.224292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:10.285349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:27.626843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:46.839578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:28.968038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:29.796976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:11.833130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:58.315433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:13.798171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:25.129547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:33.015155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:40.898532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:50.032828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:01.501159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:23.786336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:51.084237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:10.903944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:28.222140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:47.494386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:32.271827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:34.044410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:13.748142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:10:59.231662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:15.812638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:25.585154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:33.506358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:41.383375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:50.562944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:02.813504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:26.240744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:52.551037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:12.498833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:29.872313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:48.048027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:35.397547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:38.586404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:15.248761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:00.027040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:17.470190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:26.139105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:33.954815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:41.902627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:51.276307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:06.179918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:28.837395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:54.488482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:13.073306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:31.398769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:49.034512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:38.557719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:40.192877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:16.451564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:01.474734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:18.023731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:26.607108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:34.363494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:42.369645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:52.223121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:07.399938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:31.440835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:56.311302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:14.150567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:32.050099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:50.693108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:39.736789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:50.154823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:18.063770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:02.235460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:18.968028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:27.325644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:35.285379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:42.903081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:52.733881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:08.619579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:34.015639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:58.350100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:17.022670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:34.762254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:52.410768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:43.359596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:52.352057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:16:19.644759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:03.138189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:19.608333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:27.840165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:35.852983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:43.880293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:11:53.320452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:10.581185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:12:35.611121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:00.072425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:18.180303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:35.872430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:13:55.764024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:14:47.985754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-21T15:15:54.008134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-21T15:18:41.348156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ageavg_monthly_gb_downloadavg_monthly_long_distance_chargeschurn_categorychurn_reasoncontractcustomer_statusdevice_protection_plangenderinternet_serviceinternet_typelatitudelongitudemarriedmonthly_chargemultiple_linesnumber_of_dependentsnumber_of_referralsofferonline_backuponline_securitypaperless_billingpayment_methodphone_servicepremium_tech_supportstreaming_moviesstreaming_musicstreaming_tvtenure_in_monthstotal_chargestotal_extra_data_chargestotal_long_distance_chargestotal_refundstotal_revenueunlimited_datazip_code
age1.000-0.498-0.0210.0280.0410.0270.1070.0000.0000.1720.126-0.0080.0040.0290.1280.149-0.120-0.0160.0590.0000.1080.1410.0990.0230.1210.0420.2770.0000.0120.0670.0310.0100.0200.0520.033-0.007
avg_monthly_gb_download-0.4981.0000.0170.0380.0460.0120.0630.0240.0001.0000.045-0.0150.0120.096-0.0230.0440.2600.0830.0000.0560.0740.0420.0080.0220.0580.0000.1650.0150.0400.0310.0060.0210.0020.0320.013-0.008
avg_monthly_long_distance_charges-0.0210.0171.0000.0000.0000.0250.0000.0000.0360.0230.0240.021-0.0180.0000.0200.000-0.0070.0020.0000.0150.0000.0000.0161.0000.0000.0240.0030.0000.0120.0180.0170.527-0.0200.1550.0000.013
churn_category0.0280.0380.0001.0000.9960.0341.0000.0510.0000.2360.0410.1530.1200.0180.0960.0470.0500.0150.0280.0210.0870.0610.0500.0280.0200.0240.0000.0220.0000.0000.0420.0000.0000.0130.0000.150
churn_reason0.0410.0460.0000.9961.0000.0791.0000.0000.0000.3850.0000.1980.1610.0330.1100.1230.0180.0750.0740.0760.1460.0460.0690.0590.0170.0150.0000.0370.0180.0320.0000.0310.0340.0440.0000.174
contract0.0270.0120.0250.0340.0791.0000.3750.3760.0000.2020.0950.0290.0180.2810.2150.1320.1250.2210.5040.3030.3700.1500.1170.0000.4190.2610.2060.2480.4970.3460.0410.3210.0390.3600.0140.024
customer_status0.1070.0630.0001.0001.0000.3751.0000.2500.0030.2420.1560.0670.0530.2360.2250.1900.1810.2450.4320.2420.3130.1990.1610.0070.3060.1560.1360.1520.4470.3000.0380.2820.0470.3210.0200.042
device_protection_plan0.0000.0240.0000.0510.0000.3760.2501.0000.0001.0000.0000.0000.0000.1870.3580.1690.0580.1770.4170.1840.1690.0190.0690.0000.2350.2880.2350.2760.4200.4430.0290.2630.0000.4200.0040.000
gender0.0000.0000.0360.0000.0000.0000.0030.0001.0000.0000.0110.0000.0000.0000.0120.0000.0130.0000.0150.0020.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.014
internet_service0.1721.0000.0230.2360.3850.2020.2421.0000.0001.0001.0000.0600.0460.0000.9580.2730.1720.0420.0701.0001.0000.3200.2730.1711.0001.0001.0001.0000.0210.4280.1550.0450.0060.3101.0000.043
internet_type0.1260.0450.0240.0410.0000.0950.1560.0000.0111.0001.0000.0130.0360.0000.5520.2140.0690.0450.0200.0000.2110.2310.1560.4150.2020.1540.0800.1640.0020.1910.0000.1210.0000.1670.0000.014
latitude-0.008-0.0150.0210.1530.1980.0290.0670.0000.0000.0600.0131.000-0.8700.025-0.0260.0000.0290.0110.0450.0490.0500.0220.0520.0000.0350.0280.0000.0000.013-0.004-0.0020.008-0.0090.0010.0340.880
longitude0.0040.012-0.0180.1200.1610.0180.0530.0000.0000.0460.036-0.8701.0000.0210.0260.000-0.022-0.0020.0520.0000.0340.0270.0500.0000.0390.0220.0070.000-0.0120.0060.001-0.004-0.0090.0010.000-0.742
married0.0290.0960.0000.0180.0330.2810.2360.1870.0000.0000.0000.0250.0211.0000.1510.1490.3620.6820.3810.1720.1710.0080.0620.0120.1430.1450.1080.1540.3780.3240.0000.2690.0310.3330.0300.027
monthly_charge0.128-0.0230.0200.0960.1100.2150.2250.3580.0120.9580.552-0.0260.0260.1511.0000.467-0.1290.0780.1960.3210.2640.3590.2190.6890.2870.5550.4170.5510.2700.6200.1200.3070.0280.5530.010-0.006
multiple_lines0.1490.0440.0000.0470.1230.1320.1900.1690.0000.2730.2140.0000.0000.1490.4671.0000.0160.0790.3780.1610.0510.1740.1621.0000.0550.2210.1410.2150.3610.4720.0800.2680.0470.4430.0000.026
number_of_dependents-0.1200.260-0.0070.0500.0180.1250.1810.0580.0130.1720.0690.029-0.0220.362-0.1290.0161.0000.3560.0680.0860.1360.1210.0680.0290.1090.0070.0420.0210.1350.042-0.0310.0910.0180.0650.0000.017
number_of_referrals-0.0160.0830.0020.0150.0750.2210.2450.1770.0000.0420.0450.011-0.0020.6820.0780.0790.3561.0000.1780.1620.1960.0530.0540.0000.1620.0970.0890.1170.3830.327-0.0250.2570.0370.3390.0460.006
offer0.0590.0000.0000.0280.0740.5040.4320.4170.0150.0700.0200.0450.0520.3810.1960.3780.0680.1781.0000.4230.3860.0000.0970.0210.3770.3250.2640.3240.9320.5700.0480.4240.0270.5720.0000.082
online_backup0.0000.0560.0150.0210.0760.3030.2420.1840.0021.0000.0000.0490.0000.1720.3210.1610.0860.1620.4231.0000.1790.0000.0850.0040.1900.1360.1130.1460.4190.4270.0470.3020.0000.4190.0000.029
online_security0.1080.0740.0000.0870.1460.3700.3130.1690.0111.0000.2110.0500.0340.1710.2640.0510.1360.1960.3860.1791.0000.1380.1500.0350.2730.0540.0740.0410.3740.3390.0000.2410.0250.3270.0160.048
paperless_billing0.1410.0420.0000.0610.0460.1500.1990.0190.0000.3200.2310.0220.0270.0080.3590.1740.1210.0530.0000.0000.1381.0000.1850.0110.0870.1000.0520.1170.0000.1580.0430.0190.0000.1330.0000.004
payment_method0.0990.0080.0160.0500.0690.1170.1610.0690.0000.2730.1560.0520.0500.0620.2190.1620.0680.0540.0970.0850.1500.1851.0000.0240.1510.0930.0480.0950.0950.1160.0320.0720.0140.1020.0000.049
phone_service0.0230.0221.0000.0280.0590.0000.0070.0000.0000.1710.4150.0000.0000.0120.6891.0000.0290.0000.0210.0040.0350.0110.0241.0000.0390.0410.0270.0520.0000.1510.0350.3410.0000.1850.0000.027
premium_tech_support0.1210.0580.0000.0200.0170.4190.3060.2350.0001.0000.2020.0350.0390.1430.2870.0550.1090.1620.3770.1900.2730.0870.1510.0391.0000.1610.1670.1610.3740.3570.0490.2270.0240.3400.0000.024
streaming_movies0.0420.0000.0240.0240.0150.2610.1560.2880.0001.0000.1540.0280.0220.1450.5550.2210.0070.0970.3250.1360.0540.1000.0930.0410.1611.0000.8190.4340.3330.4280.0200.2410.0000.4040.0000.033
streaming_music0.2770.1650.0030.0000.0000.2060.1360.2350.0001.0000.0800.0000.0070.1080.4170.1410.0420.0890.2640.1130.0740.0520.0480.0270.1670.8191.0000.3500.2690.3420.0000.1880.0000.3240.0000.029
streaming_tv0.0000.0150.0000.0220.0370.2480.1520.2760.0001.0000.1640.0000.0000.1540.5510.2150.0210.1170.3240.1460.0410.1170.0950.0520.1610.4340.3501.0000.3250.4240.0000.2370.0000.4000.0060.000
tenure_in_months0.0120.0400.0120.0000.0180.4970.4470.4200.0000.0210.0020.013-0.0120.3780.2700.3610.1350.3830.9320.4190.3740.0000.0950.0000.3740.3330.2690.3251.0000.8890.0190.6630.0840.9130.0000.010
total_charges0.0670.0310.0180.0000.0320.3460.3000.4430.0000.4280.191-0.0040.0060.3240.6200.4720.0420.3270.5700.4270.3390.1580.1160.1510.3570.4280.3420.4240.8891.0000.0780.6500.0870.9780.0000.003
total_extra_data_charges0.0310.0060.0170.0420.0000.0410.0380.0290.0000.1550.000-0.0020.0010.0000.1200.080-0.031-0.0250.0480.0470.0000.0430.0320.0350.0490.0200.0000.0000.0190.0781.000-0.0040.0090.0670.857-0.002
total_long_distance_charges0.0100.0210.5270.0000.0310.3210.2820.2630.0270.0450.1210.008-0.0040.2690.3070.2680.0910.2570.4240.3020.2410.0190.0720.3410.2270.2410.1880.2370.6630.650-0.0041.0000.0610.7780.0000.010
total_refunds0.0200.002-0.0200.0000.0340.0390.0470.0000.0000.0060.000-0.009-0.0090.0310.0280.0470.0180.0370.0270.0000.0250.0000.0140.0000.0240.0000.0000.0000.0840.0870.0090.0611.0000.0820.015-0.004
total_revenue0.0520.0320.1550.0130.0440.3600.3210.4200.0000.3100.1670.0010.0010.3330.5530.4430.0650.3390.5720.4190.3270.1330.1020.1850.3400.4040.3240.4000.9130.9780.0670.7780.0821.0000.0000.007
unlimited_data0.0330.0130.0000.0000.0000.0140.0200.0040.0001.0000.0000.0340.0000.0300.0100.0000.0000.0460.0000.0000.0160.0000.0000.0000.0000.0000.0000.0060.0000.0000.8570.0000.0150.0001.0000.000
zip_code-0.007-0.0080.0130.1500.1740.0240.0420.0000.0140.0430.0140.880-0.7420.027-0.0060.0260.0170.0060.0820.0290.0480.0040.0490.0270.0240.0330.0290.0000.0100.003-0.0020.010-0.0040.0070.0001.000

Missing values

2024-11-21T15:16:22.737558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-21T15:16:34.110604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-21T15:16:41.269365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idgenderagemarriednumber_of_dependentscityzip_codelatitudelongitudenumber_of_referralstenure_in_monthsofferphone_serviceavg_monthly_long_distance_chargesmultiple_linesinternet_serviceinternet_typeavg_monthly_gb_downloadonline_securityonline_backupdevice_protection_planpremium_tech_supportstreaming_tvstreaming_moviesstreaming_musicunlimited_datacontractpaperless_billingpayment_methodmonthly_chargetotal_chargestotal_refundstotal_extra_data_chargestotal_long_distance_chargestotal_revenuecustomer_statuschurn_categorychurn_reason
00002-ORFBOFemale37Yes0Frazier Park9322534.827662-118.99907329NaNYes42.39NoYesCable16.0NoYesNoYesYesNoNoYesOne YearYesCredit Card65.60593.300.000381.51974.81StayedNaNNaN
10003-MKNFEMale46No0Glendale9120634.162515-118.20386909NaNYes10.69YesYesCable10.0NoNoNoNoNoYesYesNoMonth-to-MonthNoCredit Card-4.00542.4038.331096.21610.28StayedNaNNaN
20004-TLHLJMale50No0Costa Mesa9262733.645672-117.92261304Offer EYes33.65NoYesFiber Optic30.0NoNoYesNoNoNoNoYesMonth-to-MonthYesBank Withdrawal73.90280.850.000134.60415.45ChurnedCompetitorCompetitor had better devices
30011-IGKFFMale78Yes0Martinez9455338.014457-122.115432113Offer DYes27.82NoYesFiber Optic4.0NoYesYesNoYesYesNoYesMonth-to-MonthYesBank Withdrawal98.001237.850.000361.661599.51ChurnedDissatisfactionProduct dissatisfaction
40013-EXCHZFemale75Yes0Camarillo9301034.227846-119.07990333NaNYes7.38NoYesFiber Optic11.0NoNoNoYesYesNoNoYesMonth-to-MonthYesCredit Card83.90267.400.00022.14289.54ChurnedDissatisfactionNetwork reliability
50013-MHZWFFemale23No3Midpines9534537.581496-119.97276209Offer EYes16.77NoYesCable73.0NoNoNoYesYesYesYesYesMonth-to-MonthYesCredit Card69.40571.450.000150.93722.38StayedNaNNaN
60013-SMEOEFemale67Yes0Lompoc9343734.757477-120.550507171Offer AYes9.96NoYesFiber Optic14.0YesYesYesYesYesYesYesYesTwo YearYesBank Withdrawal109.707904.250.000707.168611.41StayedNaNNaN
70014-BMAQUMale52Yes0Napa9455838.489789-122.270110863Offer BYes12.96YesYesFiber Optic7.0YesNoNoYesNoNoNoNoTwo YearYesCredit Card84.655377.800.0020816.486214.28StayedNaNNaN
80015-UOCOJFemale68No0Simi Valley9306334.296813-118.68570307Offer EYes10.53NoYesDSL21.0YesNoNoNoNoNoNoYesTwo YearYesBank Withdrawal48.20340.350.00073.71414.06StayedNaNNaN
90016-QLJISFemale43Yes1Sheridan9568138.984756-121.345074365NaNYes28.46YesYesCable14.0YesYesYesYesYesYesYesYesTwo YearYesCredit Card90.455957.900.0001849.907807.80StayedNaNNaN
customer_idgenderagemarriednumber_of_dependentscityzip_codelatitudelongitudenumber_of_referralstenure_in_monthsofferphone_serviceavg_monthly_long_distance_chargesmultiple_linesinternet_serviceinternet_typeavg_monthly_gb_downloadonline_securityonline_backupdevice_protection_planpremium_tech_supportstreaming_tvstreaming_moviesstreaming_musicunlimited_datacontractpaperless_billingpayment_methodmonthly_chargetotal_chargestotal_refundstotal_extra_data_chargestotal_long_distance_chargestotal_revenuecustomer_statuschurn_categorychurn_reason
70339975-SKRNRMale24No0Sierraville9612639.559709-120.34563901Offer EYes49.51NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMonth-to-MonthNoCredit Card18.9018.900.0049.5168.41JoinedNaNNaN
70349978-HYCINMale72Yes1Bakersfield9330135.383937-119.020428147NaNYes42.29NoYesFiber Optic22.0NoYesNoNoYesNoNoNoOne YearYesBank Withdrawal84.954018.050.0801987.636085.68StayedNaNNaN
70359979-RGMZTFemale20No0Los Angeles9002234.023810-118.15658207Offer EYes36.49NoYesFiber Optic42.0NoYesNoNoYesYesYesYesOne YearYesCredit Card94.05633.450.00255.43888.88StayedNaNNaN
70369985-MWVIXFemale53No0Hume9362836.807595-118.90154401Offer EYes42.09NoYesFiber Optic9.0NoNoNoNoNoNoNoYesMonth-to-MonthYesCredit Card70.1570.150.0042.09112.24ChurnedCompetitorCompetitor had better devices
70379986-BONCEFemale36No0Fallbrook9202833.362575-117.29964404NaNYes2.01NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMonth-to-MonthNoBank Withdrawal20.9585.500.008.0493.54ChurnedCompetitorCompetitor made better offer
70389987-LUTYDFemale20No0La Mesa9194132.759327-116.997260013Offer DYes46.68NoYesDSL59.0YesNoNoYesNoNoYesYesOne YearNoCredit Card55.15742.900.00606.841349.74StayedNaNNaN
70399992-RRAMNMale40Yes0Riverbank9536737.734971-120.954271122Offer DYes16.20YesYesFiber Optic17.0NoNoNoNoNoYesYesYesMonth-to-MonthYesBank Withdrawal85.101873.700.00356.402230.10ChurnedDissatisfactionProduct dissatisfaction
70409992-UJOELMale22No0Elk9543239.108252-123.64512102Offer EYes18.62NoYesDSL51.0NoYesNoNoNoNoNoYesMonth-to-MonthYesCredit Card50.3092.750.0037.24129.99JoinedNaNNaN
70419993-LHIEBMale21Yes0Solana Beach9207533.001813-117.263628567Offer AYes2.12NoYesCable58.0YesNoYesYesNoYesYesYesTwo YearNoCredit Card67.854627.650.00142.044769.69StayedNaNNaN
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